get_package_versionGet metadata for a specific version of a Python package on PyPI. Returns the summary, required Python version, the full dependency list (requires_dist, i.e. what pip would resolve), and the downloadable files for that version. Use to inspect a pinned release like requests 2.31.0.
| Name | Required | Description | Default |
|---|---|---|---|
| name | Yes | Exact PyPI package name, e.g. "requests". | |
| version | Yes | Version string, e.g. "2.31.0". |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already indicate readOnly, idempotent, openWorld, and non-destructive behavior. The description adds value by listing the specific data returned (summary, Python version, dependency list, downloadable files), which gives the agent a clear expectation of the response content.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is three concise sentences: one for purpose, one for contents, one for usage example. Every sentence is meaningful and well-structured, with no unnecessary words.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given no output schema, the description adequately explains what is returned. It does not mention error cases or limitations, but for a simple metadata lookup with strong annotations, it is sufficiently complete.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema already has 100% coverage with clear descriptions for both parameters. The description does not add additional semantics or constraints beyond what the schema provides, so it meets the baseline without extra value.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states it retrieves metadata for a specific version of a Python package from PyPI, with a list of returned fields. It is explicit about the verb and resource, but does not explicitly differentiate from sibling tools like 'get_package' or 'list_releases', though the version-specific nature is implied.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides a usage example ('Use to inspect a pinned release like requests 2.31.0'), indicating when to use it. However, it does not mention when not to use it or point to alternative tools for other scenarios (e.g., getting latest version).
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
Most tools have clearly distinct purposes, but some overlap exists among the polymarket tools (e.g., bet_research, polymarket_arbitrage, polymarket_edges) and the ask_pipeworx variants. The detailed descriptions help distinguish them, but an agent might still misselect in those groups.
Tool names follow a mostly consistent verb_noun pattern in snake_case. Minor deviations exist, such as 'remember' vs 'recall' and the mixed use of verbs and nouns (e.g., 'ask_pipeworx' vs 'polymarket_arbitrage'), but overall the pattern is predictable.
With 35 tools, the server is on the heavy side. While each tool serves a specific purpose, the sheer number may be overwhelming, and some subsets (like the 7 polymarket tools) could potentially be consolidated. Still, the scope justifies many of them.
The server covers a wide range of functionalities: Python package management, company research, prediction markets, monitoring, memory, and data queries. Minor gaps exist (e.g., no direct tool for editing subscriptions), but the surface is generally comprehensive and well-rounded.